docs: refine README formatting

Signed-off-by: Jérôme Benoit <jerome.benoit@piment-noir.org>

diff --git a/README.md b/README.md
index 38942e8afc513e486ee0fb3c1c9383f19f5a2d67..6e9a3878fec8841ddf33a68086c65a95fbb93b7c 100644 (file)
--- a/README.md
+++ b/README.md
@@ -60,9 +60,9 @@ docker compose up -d --build
 | _Regressor model_                                              |                               |                                                                                                                                              |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
 | freqai.regressor                                               | `xgboost`                     | enum {`xgboost`,`lightgbm`,`histgradientboostingregressor`,`ngboost`,`catboost`}                                                             | Machine learning regressor algorithm.                                                                                                                                                                                                                                                                                                                                                                                                                                              |
 | _Data split parameters_                                        |                               |                                                                                                                                              |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
-| freqai.data_split_parameters.method                            | `train_test_split`            | enum {`train_test_split`,`timeseries_split`}                                                                                                 | Data splitting strategy. `train_test_split` for sequential split, `timeseries_split` for chronological split with configurable gap.                                                                                                                                                                                                                                                                                                                                                 |
+| freqai.data_split_parameters.method                            | `train_test_split`            | enum {`train_test_split`,`timeseries_split`}                                                                                                 | Data splitting strategy. `train_test_split` for sequential split, `timeseries_split` for chronological split with configurable gap.                                                                                                                                                                                                                                                                                                                                                |
 | freqai.data_split_parameters.test_size                         | 0.1 / None                    | float (0,1) \| int >= 1 \| None                                                                                                              | Test set size. Float for fraction, int for count. Default: 0.1 for `train_test_split`, None for `timeseries_split` (sklearn dynamic sizing).                                                                                                                                                                                                                                                                                                                                       |
-| freqai.data_split_parameters.n_splits                          | 5                             | int >= 2                                                                                                                                     | Controls train/test proportions for `timeseries_split` (higher = larger train set).                                                                                                                                                                                                                                                                                                                                                                                                 |
+| freqai.data_split_parameters.n_splits                          | 5                             | int >= 2                                                                                                                                     | Controls train/test proportions for `timeseries_split` (higher = larger train set).                                                                                                                                                                                                                                                                                                                                                                                                |
 | freqai.data_split_parameters.gap                               | 0                             | int >= 0                                                                                                                                     | Samples to exclude between train/test for `timeseries_split`. When 0, auto-calculated from `label_period_candles` to prevent look-ahead bias.                                                                                                                                                                                                                                                                                                                                      |
 | freqai.data_split_parameters.max_train_size                    | None                          | int >= 1 \| None                                                                                                                             | Maximum training set size for `timeseries_split`. When set, creates a sliding window instead of expanding train set. None = no limit.                                                                                                                                                                                                                                                                                                                                              |
 | _Label smoothing_                                              |                               |                                                                                                                                              |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
@@ -94,7 +94,7 @@ docker compose up -d --build
 | freqai.feature_parameters.max_label_natr_multiplier            | 12.0                          | float > 0                                                                                                                                    | Maximum labeling NATR multiplier used for reversals labeling HPO.                                                                                                                                                                                                                                                                                                                                                                                                                  |
 | freqai.feature_parameters.label_frequency_candles              | `auto`                        | int >= 2 \| `auto`                                                                                                                           | Reversals labeling frequency. `auto` = max(2, 2 \* number of whitelisted pairs).                                                                                                                                                                                                                                                                                                                                                                                                   |
 | freqai.feature_parameters.label_weights                        | [1/7,1/7,1/7,1/7,1/7,1/7,1/7] | list[float]                                                                                                                                  | Per-objective weights used in distance calculations to ideal point. Objectives: (1) number of detected reversals, (2) median swing amplitude, (3) median (swing amplitude / median volatility-threshold ratio), (4) median swing volume per candle, (5) median swing speed, (6) median swing efficiency ratio, (7) median swing volume-weighted efficiency ratio.                                                                                                                  |
-| freqai.feature_parameters.label_p_order                        | `None`                        | float \| None                                                                                                                                | p-order parameter for distance metrics. Used by `minkowski` (default 2.0) and `power_mean` (default 1.0). Ignored by other metrics.                                                                                                                                                                                                                                                                                                                                                |
+| freqai.feature_parameters.label_p_order                        | None                          | float \| None                                                                                                                                | p-order parameter for distance metrics. Used by `minkowski` (default 2.0) and `power_mean` (default 1.0). Ignored by other metrics.                                                                                                                                                                                                                                                                                                                                                |
 | freqai.feature_parameters.label_method                         | `compromise_programming`      | enum {`compromise_programming`,`topsis`,`kmeans`,`kmeans2`,`kmedoids`,`knn`,`medoid`}                                                        | HPO `label` Pareto front trial selection method.                                                                                                                                                                                                                                                                                                                                                                                                                                   |
 | freqai.feature_parameters.label_distance_metric                | `euclidean`                   | string                                                                                                                                       | Distance metric for `compromise_programming` and `topsis` methods.                                                                                                                                                                                                                                                                                                                                                                                                                 |
 | freqai.feature_parameters.label_cluster_metric                 | `euclidean`                   | string                                                                                                                                       | Distance metric for `kmeans`, `kmeans2`, and `kmedoids` methods.                                                                                                                                                                                                                                                                                                                                                                                                                   |
@@ -112,7 +112,7 @@ docker compose up -d --build
 | freqai.label_prediction.threshold_method                       | `mean`                        | enum {`mean`,`isodata`,`li`,`minimum`,`otsu`,`triangle`,`yen`,`median`,`soft_extremum`}                                                      | Thresholding method for prediction thresholds.                                                                                                                                                                                                                                                                                                                                                                                                                                     |
 | freqai.label_prediction.soft_extremum_alpha                    | 12.0                          | float >= 0                                                                                                                                   | Alpha for `soft_extremum` threshold method.                                                                                                                                                                                                                                                                                                                                                                                                                                        |
 | freqai.label_prediction.outlier_quantile                       | 0.999                         | float (0,1)                                                                                                                                  | Quantile threshold for predictions outlier filtering.                                                                                                                                                                                                                                                                                                                                                                                                                              |
-| freqai.label_prediction.keep_fraction                          | 0.5                           | float (0,1]                                                                                                                                  | Fraction of extrema used for thresholds. `1.0` uses all, lower values keep only most significant. Applies to `rank_extrema` and `rank_peaks`; ignored for `partition`.                                                                                                                                                                                                                                                                                                             |
+| freqai.label_prediction.keep_fraction                          | 0.5                           | float (0,1]                                                                                                                                  | Fraction of extrema used for thresholds. 1 uses all, lower values keep only most significant. Applies to `rank_extrema` and `rank_peaks`; ignored for `partition`.                                                                                                                                                                                                                                                                                                                 |
 | _Optuna / HPO_                                                 |                               |                                                                                                                                              |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
 | freqai.optuna_hyperopt.enabled                                 | false                         | bool                                                                                                                                         | Enables HPO.                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
 | freqai.optuna_hyperopt.sampler                                 | `tpe`                         | enum {`tpe`,`auto`}                                                                                                                          | HPO sampler algorithm for `hp` namespace. `tpe` uses [TPESampler](https://optuna.readthedocs.io/en/stable/reference/samplers/generated/optuna.samplers.TPESampler.html) with multivariate, group, and constant_liar (when multiple workers), `auto` uses [AutoSampler](https://hub.optuna.org/samplers/auto_sampler).                                                                                                                                                              |